ED-NAS:基于神经网络架构搜索的陶瓷晶粒SEM图像分割方法

ED-NAS: Ceramic Grain Segmentation Based on Neural Architecture Search Using SEM Images

为了提高深度卷积神经网络(Convolutional Neural Network,CNN)设计的自动化程度并进一步提高陶瓷晶粒扫描电子显微镜(Scanning Electron Microscope,SEM)图像分割的准确性,提出了一种基于神经网络架构搜索的陶瓷晶粒图像分割方法 .该方法设计多分支结构编码空间和链式结构解码空间,并构造多分支结构编码Cell和链式结构解码Cell;同时基于强化学习分别搜索最佳编码Cell和解码Cell;此外,基于编码-解码神经网络架构堆叠最佳Cell构建陶瓷晶粒图像分割CNN,并采用池化索引在解码阶段恢复丢失的细节信息.实验在包含了629张的陶瓷晶粒SEM图像数据集上进行,搜索最佳Cell耗时约148 GPU-时.与U-Net、SegNet等SOTA方法相比,该方法在陶瓷晶粒测试集上获得了更高的分割准确性(mIoU≈68.9%).

In order to improve the automation degree of deep convolutional neural network(CNN) design and further improve the accuracy of ceramic grainsegmentation using scanning electron microscope(SEM) images,a ceramic grain segmentation method is proposed based on neural architecture search. This method designs searching spaces including the ones of multi-branch structure for encoding and chain structure for decoding, where encoding cells(E-cell) and decoding cells(Dcell) are constructed. The best E-cell and D-cell are found using reinforcement learning. Moreover, an encoding-decoding neural architecture-based CNN is built for ceramic grain segmentation by stacking the best cells, and the pooling indices are adopted to recover the lost details in the decoding stage. The experiment was carried out on a dataset of629SEM images of ceramic grain, and the searching process took about148GPU-hours. Compared with SOTA methods such as U-Net and SegNet, the proposed method obtained higher segmentation accuracy(mIoU≈68.9%) on a ceramic grains test dataset.